The statements in this section merely provide background information related to the present disclosure. Accordingly, such statements are not intended to constitute an admission of prior art.
Active Safety and Driver Assistance Systems (ASDAS) utilize monocular vision systems as a low-cost solution for detecting a target object, such as a leading vehicle traveling along a roadway. Monocular vision systems estimate depth based on a flat ground assumption. However, when the flat ground assumption does not hold true, the depth estimation deviates from its true value. For instance, the monocular vision system may estimate the range to a target object to be further away than the target object actually is when the flat ground assumption does not hold true.
It is known, for example, to utilize range detection devices such as radar and lidar to detect the presence and range to a target object. However, such range detection devices are costly, increasing the price of a vehicle equipped with ASDAS.
It is further known, for example, to utilize stereo imaging approaches to determine the presence of a target object by extracting three-dimensional features from a disparity map of two captured images, each from a respective camera device, and matching a sparse set of key points. However, such stereo imaging approaches suffer from depth inaccuracy due to a narrow baseline between the two camera devices because depth accuracy degrades quadratically with depth. Additionally, pixel correspondence issues resulting in an unusable disparity map can occur when the baseline between the two camera devices is increased.
Accordingly, it is desirable to improve the depth accuracy using stereo imaging without extracting three-dimensional features that require the use of disparity maps.